Distribution-Free Exact High Dimensional Low Sample Size k-Sample Tests

Testing homogeneity of k multivariate distributions is a classical and challenging problem in statistics, and this becomes even more challenging when the dimension of the data exceeds the sample size. We construct some tests for this purpose which are exact level (size) alpha tests based on clustering. These tests are easy to implement and distribution-free in finite sample situations. Under appropriate regularity conditions, these tests have the consistency property in HDLSS asymptotic regime, where the dimension of data grows to infinity while the sample size remains fixed. We also consider a multiscale approach, where the results for different number of partitions are aggregated judiciously. Details are in Biplab Paul, Shyamal K De and Anil K Ghosh (2020); Soham Sarkar and Anil K Ghosh (2019) ; William M Rand (1971) ; Cyrus R Mehta and Nitin R Patel (1983) ; Joseph C Dunn (1973) ; Sture Holm (1979) ; Yoav Benjamini and Yosef Hochberg (1995) .


Reference manual

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2.0.0 by Biplab Paul, a year ago

Browse source code at https://github.com/cran/HDLSSkST

Authors: Biplab Paul [aut, cre] , Shyamal K. De [aut] , Anil K. Ghosh [aut]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports Rcpp, stats, utils

Linking to Rcpp

See at CRAN